Detection of changes of the optic disc in glaucomatous eyes:Clinical examination and image analysis with the Topcon Imagenet system

Purpose: This study was designed to evaluate the clinical agreement in the detection of optic disc changes and the ability of computerized image analysis to detect glaucomatous deterioration of the optic disc. Methods: Pairs of stereophotographs of 35 glaucomatous optic discs taken 5 years apart and of 5 glaucomatous discs photographed twice on the same day. Two glaucoma specialists examined the pairs of stereophotographs (35 cases and 5 controls) in a masked manner and judged whether the optic disc showed changes in the optic disc compatible with progression of glaucomatous damage. The stereophotographs of the five optic discs photographed twice on the same day (which by definition did not change) and of five cases judged to have deteriorated by both glaucoma specialists were analyzed by computerized image analysis with the Topcon ImageNet system. Intra- and inter-observer agreement in the detection of optic disc changes (evaluated using kappa statistic), and changes in the rim area to disc area ratio (evaluated using descriptive statistics and paired t-test). Results: Intra-observer agreement had a kappa value of 0.75 for observer 1 and 0.60 for the observer 2. Inter-observer agreement between the glaucoma specialists had a kappa value of 0.60. The image analyzer did not discriminate between controls and cases with clinically apparent glaucomatous change of the optic disc. Conclusion: Clinical agreement in detecting changes in the optic disc was moderate to substantial. Computerized image analysis with the Topcon ImageNet system appeared not to be useful in detecting glaucomatous changes of the optic disc.

Selection of burst-like transients and stochastic variables using multi-brand image differencing in the Pan-STARRS1 Medium Deep Survey

We present a novel method for the light-curve characterization of Pan-STARRS1 Medium Deep Survey (PS1 MDS) extragalactic sources into stochastic variables (SVs) and burst-like (BL) transients, using multi-band image-differencing time-series data. We select detections in difference images associated with galaxy hosts using a star/galaxy catalog extracted from the deep PS1 MDS stacked images, and adopt a maximum a posteriori formulation to model their difference-flux time-series in four Pan-STARRS1 photometric bands gP1, rP1, iP1, and zP1. We use three deterministic light-curve models to fit BL transients; a Gaussian, a Gamma distribution, and an analytic supernova (SN) model, and one stochastic light-curve model, the Ornstein-Uhlenbeck process, in order to fit variability that is characteristic of active galactic nuclei (AGNs). We assess the quality of fit of the models band-wise and source-wise, using their estimated leave-out-one cross-validation likelihoods and corrected Akaike information criteria. We then apply a K-means clustering algorithm on these statistics, to determine the source classification in each band. The final source classification is derived as a combination of the individual filter classifications, resulting in two measures of classification quality, from the averages across the photometric filters of (1) the classifications determined from the closest K-means cluster centers, and (2) the square distances from the clustering centers in the K-means clustering spaces. For a verification set of AGNs and SNe, we show that SV and BL occupy distinct regions in the plane constituted by these measures. We use our clustering method to characterize 4361 extragalactic image difference detected sources, in the first 2.5 yr of the PS1 MDS, into 1529 BL, and 2262 SV, with a purity of 95.00% for AGNs, and 90.97% for SN based on our verification sets. We combine our light-curve classifications with their nuclear or off-nuclear host galaxy offsets, to define a robust photometric sample of 1233 AGNs and 812 SNe. With these two samples, we characterize their variability and host galaxy properties, and identify simple photometric priors that would enable their real-time identification in future wide-field synoptic surveys.

Posterior Probability Modeling and Image Classification for Archaeological Site Prospection: Building a Survey Efficacy Model for Identifying Neolithic Felsite Workshops in the Shetland Islands

The application of custom classification techniques and posterior probability modeling (PPM) using Worldview-2 multispectral imagery to archaeological field survey is presented in this paper. Research is focused on the identification of Neolithic felsite stone tool workshops in the North Mavine region of the Shetland Islands in Northern Scotland. Sample data from known workshops surveyed using differential GPS are used alongside known non-sites to train a linear discriminant analysis (LDA) classifier based on a combination of datasets including Worldview-2 bands, band difference ratios (BDR) and topographical derivatives. Principal components analysis is further used to test and reduce dimensionality caused by redundant datasets. Probability models were generated by LDA using principal components and tested with sites identified through geological field survey. Testing shows the prospective ability of this technique and significance between 0.05 and 0.01, and gain statistics between 0.90 and 0.94, higher than those obtained using maximum likelihood and random forest classifiers. Results suggest that this approach is best suited to relatively homogenous site types, and performs better with correlated data sources. Finally, by combining posterior probability models and least-cost analysis, a survey least-cost efficacy model is generated showing the utility of such approaches to archaeological field survey.